P
Philip L. H. Yu
Researcher at University of Hong Kong
Publications - 173
Citations - 2209
Philip L. H. Yu is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Computer science & Ranking. The author has an hindex of 23, co-authored 143 publications receiving 1870 citations.
Papers
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Journal ArticleDOI
Empirical analysis of GARCH models in value at risk estimation
Mike K. P. So,Philip L. H. Yu +1 more
TL;DR: In this article, seven GARCH models, including RiskMetrics and two long-memory GARCH (LAMG) models, were compared in estimating 1% value at risk (VaR) estimation.
Journal ArticleDOI
Classification and ranking of fermi lat gamma-ray sources from the 3fgl catalog using machine learning techniques
P. M. Saz Parkinson,P. M. Saz Parkinson,Hang Xu,Philip L. H. Yu,David Salvetti,M. Marelli,Abraham D. Falcone +6 more
TL;DR: In this article, a number of statistical and machine learning techniques were applied to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL) according to their likelihood of falling into the two major classes of gamma emitters: pulsars (PSR) or active galactic nuclei (AGN).
Journal ArticleDOI
The effects of service climate and the effective leadership behaviour of supervisors on frontline employee service quality : a multi-level analysis
TL;DR: In this paper, the authors formulated a model to describe how service climate moderates the effects of the leadership behaviour of supervisors, and found that when the service climate was unfavourable, effective leadership behaviour played a compensatory role in maintaining performance standards towards external customers.
Book
Statistical Methods for Ranking Data
Mayer Alvo,Philip L. H. Yu +1 more
TL;DR: This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data and provides a novel and unifying approach for hypotheses testing.
Journal ArticleDOI
Regression estimator in ranked set sampling
Philip L. H. Yu,Kin Lam +1 more
TL;DR: Regression-type RSS estimators of the populationmean of Y will be proposed by utilizing this concomitant variable X in both the ranking process of the units and the estimation process when the population mean of X is known.